The run is not the outcome.
Make every action accountable.
One open control plane for consequential AI-agent actions: decision, execution, evidence, and verdict.
One plane, not scattered tooling.
Every team can call the same frontier models, and most already watch them — 89% have observability. What they can’t do is prove a run was actually right, or stop an agent before it does something irreversible.
That’s where agents fail in production: the silent success that’s secretly wrong, and the unapproved action no one can undo. Latency charts and token counts sail right past both.
Agenko gives every consequential action one durable record — what the agent proposed, what policy allowed, what executed, and what actually happened.
The problem
- 89% of teams have observability, but only 52% have any evaluation
- Silent failures slip past latency charts every day
- Most tools stop at traces, scores, or approvals
- Agenko connects those layers around the actual side effect
What we believe.
Prove and stop, don't just watch
Seeing is solved. The durable product is proving what agents actually did and stopping the irreversible ones.
Open by default
Open source, OTEL-native. Self-host in two containers.
Honesty is the product
Measured is measured. Inferred is labeled. No LLM guessing.
Build in public
Pre-launch. Spec and strategy open on GitHub.
Shubham Pitekar
Founder, Agenko
The scariest AI failures aren’t loud. They’re a 200 response that’s secretly wrong — and an irreversible action nobody paused.
I started Agenko after watching agents in production do the same two things: claim success when the consequential action never happened, and charge ahead on actions no one can undo. Teams could see everything and prove nothing.
So we’re building one open control plane around the action itself — the proposal, the decision, the execution, the evidence, the verdict. Self-hostable, built in public from day one.
Come build the boundary with us.
Read the source, open an issue, or follow the build as we ship the first receipt and gate.